Abstract

Edge detection and feature extraction play an important role in digital image processing field. It reduces the amount of data and filters out useless information while preserving the important structural properties in an image. It was observed that using the same edge detection operator for different images make some images suffer from the details (high) and missing (low) edges. This limitation may affect the features for image understanding. Hence, the aim is enhancement of the edge pixels which suffer from the details and missing edge’s pixel by adjustment edge pixel in an automatic way for different images. This paper simulates the mechanism of how our body normally controls high and low blood pressure level to regulate the features of high and low edge images. The efficiency of proposed model is demonstrated experimentally on the hand posture dataset. The recognition accuracy obtained is 98.66%. The model provides better performance than conventional methods.

Keywords

Introduction

The edge detection and feature extraction are main steps in digital
image recognition. The features extraction phase starts from the edge
detection step. Then the features vectors are created. So, the accuracy of
features vectors depends on the quality of output edge. Although, the
performances of most features extraction techniques are acceptable for
simple noise images. But, some images suffer from the more features
due to details edges as shown in Figure 1a, and sometimes low features
due to weak edges as shown in Figure 1b. Since there is a need to develop
a changeable feature extraction approach to outlining the boundaries of
hand [1].

This paper proposes an artificial blood pressure (ABP) model for
extracting the features of edge’s images. The model is inspired from
blood pressure control. The blood pressure tells the doctor how stable
or critical patients are. If the blood pressure is too high, this can cause
damage to the kidneys or the heart can fail, and low blood pressure can
also be serious and any severe fluctuations need to be addressed and
treated as soon as possible [2]. In both cases, the blood pressure must
be regulated to save a patient life.

As in images, high and low edges effect on image’s features. The
ABP model has the ability to change depending on the form of edge
image (high, normal or low edge). In the normal edge case, the features
are extracted without any change in the edge image. In the highest
edge case, the edge’s pixel will be decreased according to neighbour
edge’s pixels. In the missing edge case, the edge’s pixel will be increased
according to neighbour edge’s pixels.

The rest of the paper is organized as follows. Section 4 describes
the brief information regarding the blood pressure regularization in
the human body. Section 5 presents the basic model structure. Section
6 gives the experimental results and their analysis. Finally, concluding
remarks are summarized in Section 7.

Biologically Strategy for Normal Control Blood Pressure

Blood pressure (P) is the force of blood pushing against the walls of
arteries [3]. Typically, P is recorded as two numbers, written as a ratio,
are systolic (Ps) and diastolic (Pd). Ps is the top number, measures the
pressure in the arteries when the heart beats (when the heart muscle contracts) and Pd are the bottom number, measures the pressure in the
arteries between heartbeats (when the heart muscle is resting between
beats and refilling with blood). The normal resting blood pressure in an
adult is approximately Ps/Pd=120/80 mm Hg (millimeters of mercury)
[4,5]. Normally, P is controlled by changing cardiac output (CO) and
total peripheral resistance (R) [6], as presented by the formula.

P=CO × R (1)

The cardiac output (CO) is controlled by blood flow (V) and heart
rate (h) [6], as represented by the formula.

CO=V × h (2)

Thus,

P=V × h × R (3)

Under the assumption of that heart rate is equal to one beat and the
resistance equal one in normal blood vessel, then,

P=V (4)

The blood flow (V) is the continuous circulation of blood in the
cardiovascular system. Blood flow representing the change in the arterial
volume is given by the difference between the rate of flow entering the
aorta and the rate of flow from the aorta [7,8]. The mathematical model
for blood flow is given by:

(5)

Where Vd is the end diastolic volume and Vs is the end systolic
volume. Thus, the relationship between blood flow and blood pressure
depends on the vascular system and obeys an adaptation of Ohm’s law
[9], known as Darcy’s law [10]. That is:

(6)

Thus, the change in blood pressure is the difference between the
diastolic and the systolic blood volume numbers. If the blood pressure
is, 120/80 then the change is 40. If the pressure change is “wide”,
meaning there is a very large difference between the top and bottom
numbers, it indicates that something is going on in the body such as
hyperthyroidism, shock or trauma, or any condition that relaxes the
blood vessels too much. If the pressure change is a few numbers,
this indicates things like blood loss, rapid heart rate, or congestive heart failure, as represented by formula 7. This number is extremely
important in the critical care setting, especially when monitoring fluid
or blood loss in trauma patients [11] as shown in Figure 2.

Mapping Blood Pressure to Feature Extraction

The proposed artificial blood pressure (ABP) model is introduced
in the context of automatic adaptive feature extraction problem. The
probability of false edges considered as high value and the probability of
missing edges considered as low value. Specifically, the model simulates
the normal regularization of high and low blood pressure by extracting
forced features from high and low edge image. In comparison, pixel
flow in edge image is the same way the blood flow and the force of
features are in a similar manner to blood pressure. A further explanation
of similarity between the blood pressure regularization process and
adaptive feature extraction process of an image is shown in Figure 3.

Algorithm 1 depicts the process of adaptive feature extraction from
an image. The algorithm begins by extracting the edges of an object
using the Canny operator [12]. The output of edge detection phase
produces one of the following cases:

• High edges, in which some of the edge pixels in the image are
details. This case simulates the fact that systolic pressure is the highest
arterial blood pressure of a cardiac cycle.

• Low edges, in which some of the edge pixels in the image are
weak. In a blood pressure reading, the diastolic pressure is specifically
the lowest arterial pressure during relaxation and dilatation of the
ventricles of the heart when the ventricles fill with blood.

• Normal edges, in which image does not suffer from detail or
weak edge. This case simulates normal blood pressure.

Then, find the neighbour pixels for each edge pixel that has the same x-value position and have convergent y-value in the result from
edge detection phase. Computationally, the process usually begins
with the pixel in the first row and first column of the input image and
proceeds in raster-scan order. However, any convenient scan sequence
can be used.

Next, decompose the neighbour edge pixels to high feature Fhigh
(the next edge pixel) and low features Flow (the pervious edge pixel) as
shown in Figure 4. For example, if the edge pixel is E(10,5) and the
neighbour edge pixels are E1=(10,3), E2=(10,9). Then, the pixel with the
maximum x-position of the edge pixel is Fhigh=E2=(10,9). The pixel with
the minimum x-position is Flow=E1=(10,3). This step is motivated by the
fact that the blood pressure rises and falls throughout the day.

After that, subtracting the two features vectors . This
step is motivated by the fact that the blood flow (V) is the difference
between the rate of flow entering the aorta and the rate of flow from
the aorta. The output reflects the force of a feature pixel. A “force”
feature, in our terms, is determined by calculating the distance between
higher and lower edge pixel. Let d is the distance of good localization of
edge pixel, and it is determined as follows.

equals d, this means that the edge pixel is a good feature
representing the object.

less than d, which means that this pixel is a details feature. So,
decrease the edge pixels by ignoring Flow and Fhigh. This step is inspired
by the fact that if the pressure is high, the blood pressure is reduced by
decreasing cardiac output (CO) via a decrease in heart rate (HR).

greater than d, this means that the pixel is a weak edge. Then increase the edge pixels by adding new pixels. This step is inspired by
the fact that if the pressure is low, the blood pressure is increased by
increasing cardiac output and increased total peripheral resistance.
Depending on the edge of the input image, (some of the input image
pixel positions might be skipped, thereby saving computation time).

Experiment and Results

The Sign Language Recognition (SLR) system is used in order to
illustrate the ABP model. The System consists of mainly three phases
are image pre-processing, feature extraction and classification. In the
system, the sign is taken from browser window as shown in Figure 5
and the output is its meaning of the sign. The system is implemented in
MATLAB 7.0 and runs on 2 GHZ and 2.0 GB RAM.

Figure 5: Browser window for selecting sign.

Dataset description

The signs used in the experiments are the numbers from 1 to 5 in ASL as shown in Figure 6. The signs are collected from [13-15]. The ASL
signs for numbers should be signed with the dominant hand. The palm
may face toward or away from the signer depending on the preference
of the signer. The numbers from 1 to 5 in ASL can be described as [14]:

Figure 6: The number from 1 to 5 in ASL.

• Number (1): Form the hand into a fist with the index finger
pointed straight up.

• Number (2): Form the hand into a fist with the index and middle
fingers pointed straight up.

• Number (3): Form the hand with the thumb, index finger, and
middle pointed straight. The index and middle fingers point straight up.
Fold the ring and pinky fingers onto the palm of the hand.

• Number (4): Form the hand with the four fingers pointed straight
up. Fold the thumb onto the palm of the hand.

• Number (5): Form the hand with all five digits pointed straight
and held apart from each other.

Image pre-processing

The images are needed to pre-processing before tested [15]. The
pre-processing of the input image is summarized as follows. First, a
skin color detection approach is applied [16]. Then convert the image
to grey level and remove noise using a median filter [17]. After that,
detect hand edges using canny algorithm [18] as shown in Figure 7.
Finally, resize the image to 100 × 100 which reduced the image’s data
without change its details.

The ABP proposed model is applied to the image of hand edge.
Visual results of ABP model are shown in Figures 8 and 9; the two sets
of images can lead us to the subjective evaluation of the performances
of the proposed algorithm.

Figure 8: Number “4” in ASL. (a) Original image. (b) The edge image confused with duplicated contour. (c) The result of the ABP algorithm.

Figure 9: Number “5” in ASL. (a) Original image. (b) The edge image suffers from misshaping some of the lines. (c) The result of the ABP model.

Figure 8 is a picture of a number “4” in ASL. The Canny edge detector has very sharp edges and duplicated contour. Applying
dynamical feature extraction technique to a noisy edge image shows
the ability to handle details edge in the image.

Figure 9 is a picture of a number “5” in ASL. The edge detectors
had problems detecting the different ridges of the cliff. There are a lot
of discrepancies in hand fingers, but no clear edges. It does better for
some features (i.e., the fingers), it still suffers from misshaping some of
the lines. A proposed algorithm constructed using individually selected
points would still work better.

Recognition phase

The output of features extraction phase is sent to recognize sign
meaning. A standard feed forward back-propagation neural network is
used to classify signs. The network consists of three layers. The network
based on supervised learning with sigmoid transfer functions. The
training chart for NN is shown in Figure 10.

Figure 10: Training chart for 5 signs and 75 samples.

Results

Table 1 displays a confusion matrix showing the efficiency of
recognition by ANN. Each sign has 21 samples as shown in Table 1.
The system performs that, number ‘2’ has 19% confusion to number ‘3’.
Number ‘3’ has 23% confusion to ‘5’. Numbers ‘1’, ‘4’and ‘5’ has not been
confused by any numbers.

1

2

3

4

5

1

21

0

0

0

0

2

1

16

4

0

0

3

2

0

14

0

5

4

0

0

0

21

0

5

0

0

0

0

21

Table 1: Confusion matrix for various numbers of ASL.

The performance of the system is evaluated based on its ability to
correctly classify samples to their corresponding classes. The metric
that we use to accomplish this job is called the recognition rate. The
recognition rate is defined as the ratio of the number of correctly
classified samples to the total number of samples. The dataset contains
142 signs are divided into training and testing set, 75 samples will be
used for training purpose while remaining 67 were used for testing.

(8)

The results have been observed in two different ways as shown in
Table 2. In the First result, the recognition phase has achieved using the
canny method and artificial neural network (without using proposed
models). Second, the recognition phase has used the ABP proposed
model and ANN. From the Table 2, the different methods give different
result in terms of recognition rate. The first experiment the result suffers
from low recognition rate because using ANN without any processing
of input features. The second method achieved to high recognition rate.

Model used

Canny+ANN

ABP+ANN

Train

Test

Train

Test

Correctly recognized

33

32

74

57

Recognized rate (%)

44.0

47.76

98.66

85.07

Table 2: Cross-validation of ABP model for ASL dataset.

The practical significance of these results is emphasized by
comparing them with other methods, which are closed to our system as
shown in Table 3. The experiment of our model still, had significantly
higher recognition accuracy.

Not considered as complete sign language recognizer
Information about other body parts is not sufficient

EOH + SVM based[1]

93.75%

A little change in orientation of input gestures makes a significant
change in the feature vectors and offers reduced rates of recognition.

Krawtchout+minimumdistanceClassifier [2]

95.42%

there are prevalently misclassified in sign ‘2’,’3’and’4’.

Leap Motion and Intel RealSense+SVM

72.1%

Some sign representations must include additional data, i.e., orientation of the palm.

k-NN+SVM[4]

90.1%

The system gives 80.8% recognition rate for ambiguous gestures.

Table 3: A comparative study with the result of other approaches.

Conclusion

The study was set out to solve the problem of sharp discontinuities
edges of objects, which provides low-level features for image
understanding. The ABP model is introduced for controlling high and
low features in an image. The model is inspired from how our body
normally controls high and low blood pressure level. Experiments
showed that the proposed model provides better localization features
results by decreasing false positives. Indeed, the model has the ability to change depending on the form of edge image (highest, normal and
lowest edge). The extracted features from the proposed model provide
valuable features for outline the boundaries of ASL. The future work
mainly concentrates on developing the model for more accurate and
fast feature extraction.